Sparrow Loader example

This example shows how to prepare basic data for Sparrow import. You will need a Python installation (version 3.9 or above) and an environment to run Jupyter notebooks (e.g., the jupyter CLI, JupyterLab, or Visual Studio Code).

Before running the notebook, you need to install the sparrow-loader Python module, as such:

pip install sparrow-loader

You can test that everything installed correctly by running the sparrow-loader command.

Loader schemas

"Loader schemas" are the basic recipes for building JSON objects to feed to Sparrow.

You can run this command to show loader schemas. It's a bit overwhelming, but we're working on it.

sparrow-loader show-schemas

You can show a specific schema by running

sparrow-loader show-schema sample

Validating data

Validating complex data is easier in Python, so head over to the notebook!

Importing data into Sparrow's database

Right now, to easily import data into Sparrow, you must be running it on your own machine. The Sparrow installation guide can help with this, but briefly, the steps are:

1. Install Docker and download the Sparrow executable. The easiest way is to use the following Terminal command:

   bash -c "$(curl -fsSL https://sparrow-data.org/get-sparrow.sh)"
   

2. Enter the loader-test-lab directory to enter the lab context. Running the sparrow command should show the name "Loader test lab"
3. You can then run sparrow up to start Sparrow (this command can be exited with Ctrl+C after the server starts).
4. In order to import data, you must run the sparrow create-user command to create an admin user.
5. Finally, navigate to http://localhost:5002 to load the Sparrow web application.

Building more serious lab workflows

Most labs have sophisticated processes to manage legacy data and the collection and reduction of new data. Integration with these pipelines requires the development and maintenance of ingestion pipelines that work with different types of geochemical data. Typically, these import tools are written in Python. Lab configurations are often version-controlled and stored on GitHub, which facilitates development within the lab and sharing within the community.

Here are some example pipelines:

• WiscAr lab configuration, which runs the WiscAr argon lab's Sparrow website.